seminr estimate_lavaan_ten_berge() function
Estimates factor scores using ten Berge method for a fitted Lavaan model
estimate_lavaan_ten_berge(fit)
fit |
A fitted |
A list with two elements: ten berge scores; weights for calculating scores
#' #seminr syntax for creating measurement model mobi_mm <- constructs( reflective("Image", multi_items("IMAG", 1:5)), reflective("Quality", multi_items("PERQ", 1:7)), reflective("Value", multi_items("PERV", 1:2)), reflective("Satisfaction", multi_items("CUSA", 1:3)), reflective("Complaints", single_item("CUSCO")), reflective("Loyalty", multi_items("CUSL", 1:3)) ) #seminr syntax for freeing up item-item covariances mobi_am <- associations( item_errors(c("PERQ1", "PERQ2"), "IMAG1") ) #seminr syntax for creating structural model mobi_sm <- relationships( paths(from = c("Image", "Quality"), to = c("Value", "Satisfaction")), paths(from = c("Value", "Satisfaction"), to = c("Complaints", "Loyalty")), paths(from = "Complaints", to = "Loyalty") ) # Estimate model and get results cbsem <- estimate_cbsem(mobi, mobi_mm, mobi_sm, mobi_am) tb <- estimate_lavaan_ten_berge(cbsem$lavaan_output) tb$scores tb$weights
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